plan-ml-review

An ML architecture review evaluates data modality, dataset scale, model choices, training plans, evaluation protocols, and compute budgets. It generates an Architecture Decision Record and a risk-aware deployment plan suitable for stakeholder approval.

An ML training and evaluation plan should include learning rate schedules, gradient norm diagnostics, warmup phases, per-class metrics, and distribution shift analysis. It must also define checkpoint strategies and robustness checks to ensure reliable model convergence.

Assess compute budget and deployment constraints by mapping data modality and dataset scale against training requirements. This diagnostic checklist identifies resource bottlenecks and defines governance rules like versioning, monitoring, and retraining triggers for production.

Yes, the structured review process supports multi-modal ML models by performing data-modality mapping and analyzing multi-modal fusion tradeoffs. It evaluates backbone design and task-head architecture to ensure the deployment strategy handles diverse data inputs.

You need a formal Architecture Decision Record for machine learning when stakeholder review requires rigorous justification of model choices, training plans, and deployment governance. It documents tradeoff analysis and risk-aware planning across the full project lifecycle.